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Chapter: Ongoing Challenges in Face Recognition

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Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
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Ongoing Challenges in Face Recognition

PETER N. BELHUMEUR

Columbia University

New York, New York

FIGURE 1 The same individual imaged with the same camera and with nearly the same facial expression and pose may appear dramatically different with changes in lighting conditions. The first two images were taken indoors; the third and fourth images were taken outdoors. All four images were taken with a Canon EOS 1D digital camera. Before each picture was taken, the subject was asked to make a neutral facial expression and to look directly into the lens.

“[V]ariations between the images of the same face due to illumination and viewing direction are almost always larger than image variations due to changes in face identity” (Moses et al., 1994). As Figure 1 shows, the same person, with the same facial expression, can appear strikingly different with changes in the direction of the light source and point of view. These variations are exacerbated

Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×

by additional factors, such as facial expression, perspiration, hair style, cosmetics, and even changes due to aging.

The problem of face recognition can be cast as a standard pattern-classification or machine-learning problem. Imagine we are given a set of images labeled with the person’s identity (the gallery set) and a set of images unlabeled from a group of people that includes the individual (the probe set), and we are trying to identify each person in the probe set. This problem can be attacked in three steps. In the first step, the face is located in the image, a process known as face detection, which can be as challenging as face recognition (see Viola and Jones, 2004, and Yang et al., 2000, for more detail). In the second step, a collection of descriptive measurements, known as a feature vector, is extracted from each image. In the third step, a classifier is trained to assign a label with a person’s identity to each feature vector. (Note that these classifiers are simply mathematical functions that return an index corresponding to a subject’s identity.)

In the last few years, numerous feature-extraction and pattern-classification methods have been proposed for face recognition (Chellappa et al., 1995; Fromherz, 1998; Pentland, 2000; Samil and Iyengar, 1992; Zhao et al., 2003). Geometric, feature-based methods, which have been used for decades, use properties and relations (e.g., distances and angles) between facial features, such as eyes, mouth, nose, and chin, to achieve recognition (Brunelli and Poggio, 1993; Goldstein et al., 1971; Harmon et al., 1978, 1981; Kanade, 1973, 1977; Kaufman and Breeding, 1976; Li and Lu, 1999; Samil and Iyengar, 1992; Wiskott et al., 1997). Despite their economical representation and insensitivity to small variations in illumination and point of view, feature-based methods are quite sensitive to the feature-extraction and measurement process, the reliability of which has been called into question (Cox et al., 1996) In addition, some have argued that face recognition based on inferring identity by the geometric relations among local image features is not always effective (Brunelli and Poggio, 1993).

In the last decade, appearance-based methods have been introduced that use low-dimensional representations of images of objects or faces (e.g., Hallinan, 1994; Hallinan et al., 1999; Moghaddam and Pentland, 1997; Murase and Nayar, 1995; Pentland et al., 1994; Poggio and Sung, 1994; Sirovitch and Kirby, 1987; Turk and Pentland, 1991). Appearance-based methods differ from feature-based techniques in that low-dimensional representations are faithful to the original image in a least-squares sense. Techniques such as SLAM (Murase and Nayar, 1995) and Eigenfaces (Turk and Pentland, 1991) have demonstrated that appearance-based methods are both accurate and easy to use. The feature vector used for classification in these systems is a linear projection of the face image in a lower dimensional linear subspace. In extreme cases, the feature vector is chosen as the entire image, with each element in the feature vector taken from a pixel in the image.

Despite their success, many appearance-based methods have a serious drawback. Recognition of a face under particular lighting conditions, in a particular

Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×

pose, and with a particular expression is reliable only if the face has been previously seen under similar circumstances. In fact, variations in appearance between images of the same person confound appearance-based methods. To demonstrate just how severe this variability can be, an array of images (Figure 2) shows variability in the Cartesian product of pose × lighting for a single individual.

If the gallery set contains a very large number of images of each subject in many different poses, lighting conditions, and with many different facial expressions, even the simplest appearance-based classifier might perform well. However, there are usually only a few gallery images per person from which the classifier must learn to discriminate between individuals.

In an effort to overcome this shortcoming, there has been a recent surge in work on 3-D face recognition. The idea of these systems is to build face-recognition systems that use a handful of images acquired at enrollment time to estimate models of the 3-D shape of each face. The 3-D models can then be used to render images of each face synthetically in novel poses and lighting conditions—effectively expanding the gallery set for each face. Alternatively, 3-D models can be used in an iterative fitting process in which the model for each face is rotated, aligned, and synthetically illuminated to match the probe image. Conversely, the models can be used to warp a probe image of a face back to a canonical frontal point of view and lighting condition. In both of these cases, the identity chosen corresponds to the model with the best fit.

The 3-D models of the face shape can be estimated by a variety of methods. In the simplest methods, the face shape is assumed to be a generic average of a large collection of sample face shapes acquired from laser range scans. Georghiades and colleagues (1999, 2000) estimated the face shape from changes in the shading in multiple enrollment images of the same face under varying lighting conditions. Kukula (2004) estimated the shape using binocular stereopsis on two enrollment images taken from slightly different points of view. Ohlhorst (2005) based the estimate on deformations in the grid pattern of infrared light projected onto the face. In another study, Blanz and Vetter (2003) inferred the 3-D face shape from the shading in a single image using a parametric model of face shape. Often, a “bootstrap” set of prior training data of face shape and reflectance taken from individuals who are not in the gallery or probe sets is used to improve the shape and reflectance estimation process.

The 3-D face recognition techniques described above constitute just a small sampling of the work going on in this area, much of it too new to appear in surveys. To give the reader an idea of the potential of these approaches, the seven images (Figure 3, top row) under variable lighting conditions are used to estimate the face shape and reflectance. The estimate is then used to synthesize images of the face (Figure 4) under the same conditions as those shown in Figure 2. Note that much of the variation in appearance in pose and lighting can be inferred from as few as nine gallery images.

Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×

FIGURE 2 Images of a single individual from the Yale Face Database B showing variability due to differences in illumination and pose. The images are divided into four subsets (1 through 4 from top to bottom) according to the angle formed by the light source and the camera axis. Every pair of columns shows the images of a particular pose (1 through 9 from left to right).

Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×

FIGURE 3 A variation of photometric stereopsis was used to compute the shape and reflectance of the face in the bottom row based on the seven gallery images in the top row. Source: Georghiades et al., 1999. Reprinted with permission.

Although recent advances in 3-D face recognition have gone a long way toward addressing the complications causes by changes in pose and lighting, a great deal remains to be done. Natural outdoor lighting makes face recognition difficult, not simply because of the strong shadows cast by a light source such as the sun, but also because subjects tend to distort their faces when illuminated by a strong light; compare again the indoor and outdoor expressions of the subject in Figure 1. Furthermore, very little work has been done to address complications arising from voluntary changes in facial expression, the use of eyewear, and the more subtle effects of aging. The hope, of course, is that many of these effects can be modeled in much the same way as face shape and reflectance and that recognition will continue to improve in the coming decade.

Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×

FIGURE 4 Synthesized images of the same individual, under the same illumination and from the same point of view as in Figure 2. Once again, the synthesized images are divided into four subsets (1 through 4 from top to bottom) according to the angle of the light source direction and the camera axis. Every pair of columns shows the images from a particular pose (1 through 9 from left to right). Note that all of the images were generated synthetically from seven gallery images with frontal pose.

Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×

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Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
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Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
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Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
Page 5
Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
Page 6
Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
Page 7
Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
Page 8
Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
Page 9
Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
Page 10
Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
Page 11
Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
Page 12
Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
Page 13
Suggested Citation:"Ongoing Challenges in Face Recognition." National Academy of Engineering. 2006. Frontiers of Engineering: Reports on Leading-Edge Engineering from the 2005 Symposium. Washington, DC: The National Academies Press. doi: 10.17226/11577.
×
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